Department of Genetics, Stanford University, Stanford, CA, USA.
Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA.
Nat Genet. 2018 Jan;50(1):151-158. doi: 10.1038/s41588-017-0004-9. Epub 2017 Dec 11.
The excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable splicing events from short-read RNA-seq data and finds events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both to detect differential splicing between sample groups and to map splicing quantitative trait loci (sQTLs). Compared with contemporary methods, our approach identified 1.4-2.1 times more sQTLs, many of which helped us ascribe molecular effects to disease-associated variants. Transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at a 5% false discovery rate by an average of 2.1-fold compared with that detected through the use of gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available online.
内含子从 pre-mRNA 中的切除是 mRNA 加工的一个重要步骤。我们开发了 LeafCutter 来研究内含子剪接的样本和群体变异。LeafCutter 可从短读长 RNA-seq 数据中识别可变剪接事件，并发现具有高复杂度的事件。我们的方法避免了对转录本注释的需求，并解决了在复杂剪接事件中估计相对异构体或外显子使用的挑战。LeafCutter 既可以用于检测样本组之间的差异剪接，也可以用于映射剪接数量性状基因座 (sQTL)。与当代方法相比，我们的方法识别出的 sQTL 多了 1.4-2.1 倍，其中许多 sQTL 帮助我们将与疾病相关的变异与分子效应联系起来。通过 LeafCutter 内含子定量与 40 种复杂性状之间的全转录组关联，与仅使用基因表达水平相比，在 5%的错误发现率下，与疾病相关的基因数量平均增加了 2.1 倍。LeafCutter 速度快、可扩展、易于使用，并可在线使用。
